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  • [Python-announce] ANN: NumExpr 2.8.6 Released

    From Robert McLeod@robbmcleod@gmail.com to comp.lang.python.announce on Tue Sep 12 14:56:08 2023
    From Newsgroup: comp.lang.python.announce

    Hi everyone,

    NumExpr 2.8.6 is a release to deal with issues related to downstream
    `pandas`
    where the sanitization blacklist was hitting private variables used in their evaluate. In addition the sanitization was hitting on scientific notation.

    For those who do not wish to have sanitization on by default, it can be
    changed
    by setting an environment variable, `NUMEXPR_SANITIZE=0`.

    If you use `pandas` in your packages it is advisable you pin

    `numexpr >= 2.8.6`

    in your requirements.

    Project documentation is available at:

    http://numexpr.readthedocs.io/

    Changes from 2.8.5 to 2.8.6
    ---------------------------

    * The sanitization can be turned off by default by setting an environment variable,

    `set NUMEXPR_SANITIZE=0`

    * Improved behavior of the blacklist to avoid triggering on private
    variables
    and scientific notation numbers.


    What's Numexpr?
    ---------------

    Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated
    and use less memory than doing the same calculation in Python.

    It has multi-threaded capabilities, as well as support for Intel's
    MKL (Math Kernel Library), which allows an extremely fast evaluation
    of transcendental functions (sin, cos, tan, exp, log...) while
    squeezing the last drop of performance out of your multi-core
    processors. Look here for a some benchmarks of numexpr using MKL:

    https://github.com/pydata/numexpr/wiki/NumexprMKL

    Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that
    don't want to adopt other solutions requiring more heavy dependencies.

    Where I can find Numexpr?
    -------------------------

    The project is hosted at GitHub in:

    https://github.com/pydata/numexpr

    You can get the packages from PyPI as well (but not for RC releases):

    http://pypi.python.org/pypi/numexpr

    Documentation is hosted at:

    http://numexpr.readthedocs.io/en/latest/

    Share your experience
    ---------------------

    Let us know of any bugs, suggestions, gripes, kudos, etc. you may
    have.

    Enjoy data!
    --
    Robert McLeod
    robbmcleod@gmail.com
    robert.mcleod@hitachi-hightech.com
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